

Level 1 moderator approach 

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Hi, I am hoping someone may be able to provide some guidance as I don't think my analysis is common in the literature. Variables in data set: One manager variable: X  is a TScore. There is NO within group variance in X. Subordinate variables (2): Y & M  where Y is the outcome variable and M is a proposed moderator variable. Other variables: Cluster (work teams) and XM (Interaction variable  X*M) All variables are continuous. It is theorised that the level 2 variable (X) should predict the Level 1 variable (Y). M should moderate this relationship. Therefore I have a Level 1 moderator for a crosslevel relationship. (M is theorised not to predict Y.) I believe I need to adopt a means as outcome approach. From this there is a significant main effect (i.e. X>Y). Indeed, this relationship is significant. However, given that there is no variance at the slopes between teams, I am not sure the approach I should take in regards to modelling the moderation. I think I need to assess how M moderates the grand slope but I am unsure of this. Would I regress the model of the main relationship X>Y onto XM? Apologises if there are errors in my thinking. Kind regards Matt 


You could use the between part of M to moderate X > Y on the between level. Either the observed cluster mean of M or the latent part decomposed by Mplus (see UG Chapter 9). But there isn't much going on on Within  you have M and Y but you say M doesn't predict Y. You could estimate their variances on Within. Or, you could skip within and do a singlelevel analysis for between units. 

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